Rotavirus (RoV) infections have emerged as a significant public health concern around the world. Understanding the relationship between climatic conditions and hospitalisations due to RoV infections can help engage effective prevention strategies. This study aims to investigate the potential associations between meteorological variability and RoV-related hospitalisations in Kuala Lumpur, Malaysia. Hospitalization data from a tertiary teaching hospital in Kuala Lumpur over a twelve-year period were retrospectively collected. Concurrently, meteorological data were obtained from the Malaysian Meteorological Department (MetMalaysia) including variables of temperature, humidity, precipitation, and atmospheric pressure to further demonstrate relationship with RoV-associated hospitalisations. The results indicated positive correlations between increased rainfall, rainy days, humidity, and RoV-related hospitalisations, suggesting the influence of environmental factors on the transmission of RoV. This study highlights positive associations between meteorological variations and hospitalizations for RoV infections in Kuala Lumpur, Malaysia. Further investigations, including national-level data, are needed to deepen our understanding of these associations, particularly within the context of Malaysia and to develop targeted interventions for disease prevention and control.

Background: Serum protein electrophoresis (SPE) and immunofixation electrophoresis (IFE) are essential in diagnosing multiple myeloma (MM). Machine learning (ML) methods have emerged as a potential approach to the automation process of electrophoresis interpretation to improve diagnostic efficiency and improve turnaround time. This scoping review mapped current applications of ML in the diagnosis and classification of MM, with emphasis on automated SPE and IFE interpretation. Methods: PubMed, Web of Science, and Scopus databases were searched following PRISMA-ScR guidance. Articles published between January 2015 and April 2025 applying ML to MM diagnosis were included, and findings were synthesised descriptively and thematically. Results: Thirteen studies were included, identifying two dominant application domains: ML-based analysis of IFE images and SPE patterns. Deep learning models, particularly convolutional neural networks, achieved expert-level performance in IFE analysis, with peak accuracies reaching 99.82% and F1-scores frequently exceeding 0.90. For SPE interpretation, both deep learning and classical tree-based models demonstrated robust diagnostic yields, achieving Areas Under the Curve (AUC) between 0.90 and 0.99 and overall accuracies ranging from 82.8% to 99.1%. Notably, top-performing models recorded F1-scores up to 0.98, and in some instances, they surpassed the precision of human expert panels. Conclusions: Explainable AI methods were increasingly incorporated, though external validation, multimodal integration, explainability, and real-world deployment were limited. ML demonstrates potential to augment SPE and IFE interpretation in MM diagnostics. Future work should focus on multicentre validation, explainability, and clinical integration to support safe implementation.

Collaborative communication and teamwork are core competencies in medical education, yet they remain difficult to assess objectively during Problem-Based L